The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Weather forecasting is the application of science, technology, and statistics to predict the state of the atmosphere for a given location at some future point in time. The endeavor to fully understand Earth's climate system and to predict the weather has been a goal of humanity for millennia. Weather forecasts are typically made by collecting quantitative data about the current state of the atmosphere at a given place and using the data to drive a simulation or physical model of the atmosphere to predict how the atmosphere will change over a given period of time. For example, identifying changes in environmental variables such as temperature, air currents, barometric pressure, moisture, and so forth.
The collection of the quantitative data is performed by using various tools, such as satellite image data, weather stations, temperature readings, humidity detectors, and so forth. Physical models used for weather forecasting decompose the earth (or other geographical region) into a uniform grid where the various environmental variables are given a particular value at each location within the grid. The physical model then runs through the grid simulating the physical processes that cause changes in weather over time to reach a future predicted state. However, the base data collected from the various weather stations do not fully cover the grid, nor are the readings taken at uniform times or with tools that have identical measurement errors. In fact, in most cases there are far more points on the grid where the environmental variables are unknown than known. As a result, to convert the base observations into values for each point on the grid, a process known as data assimilation is performed which uses a combination of information to fill in the points where observations are not explicitly available. The result is a value for each of the environmental variables for each point in the grid, which is collectively referred to as an “analysis”. The analysis is then used to set the initial state of the physical model simulation which is stepped forward in time to predict the weather at some future time. In some cases, a forecast model is also used to fill in the informational gaps, which is referred to as an analysis/forecast cycle. In essence, an initial condition is set by an analysis, a forecast is run from the analysis, and the forecast is then used to fill in or smooth out the gaps in the next analysis in a repeating cycle.
Since the initial condition of the atmosphere generated by the analysis is uncertain due to the observational data being incomplete, climate scientists will often run forecasts using a set of different initial states based on the known or estimated error of an analysis. The resulting forecasts, each representing the future state of the atmosphere assuming that the values of the environmental variables in the grid were in a slightly different initial state is referred to as a forecast ensemble. The overall behavior of the ensemble, rather than simply one forecast, is then used to better capture the uncertainty in the forecast.
In most cases, the analyses are performed by government agencies (and in some cases private agencies) and made available via various databases, for example the U.S. Climate Forecast System (CFS), the European Centre for Medium-Range Weather Forecasts (ECMWF), and so forth, which provide analyses that can be accessed and used for research by weather scientists. These organizations often provide analyses at different granularities of time, such as six hours, daily, weekly, and so forth, as well as at different geographical granularities (for example different grid sizes).
Data assimilation techniques and forecasting models constantly evolve over time as atmospheric scientists develop a better understanding of the physical processes governing atmospheric evolution. As a result, if one were to view the analyses taken by various public and private organizations over an extended period of time (for example the last thirty-forty years), the changes in the data assimilation technique or forecasting model used can have a drastic impact on the analysis and the resulting forecast. To combat the non-uniformity of the techniques used to create the original analysis, climate monitoring organizations will often go back to the original observation data collected over a past period of time and apply a consistent data assimilation technique (usually one that is more up-to-date than the original technique) from that past period of time to the present. As a result, the inconsistencies are removed and the skill of forecasting models can be more easily evaluated. An analysis that is produced in this manner is referred to as a “reanalysis” since the data is being reanalyzed using a consistent technique.
Evaluating the skill of a forecast model requires a significant amount of forecasted predictions and corresponding observations with which to compare those predictions. However, when testing a new model, it is impractical to train on historical observation data and then evaluate at some point in the future based upon the analysis generated at that time. Especially for longer range forecasts, it might take over a month before a given forecast can be evaluated, and years or decades before enough data can be collected to tell whether the forecast model is actually skillful. As a result, climate scientists often perform “reforecasts”, which is a forecast based on past analyses (or more preferably reanalyses). For example, if a reanalysis covers the past 30 years, a forecast model can be initialized from those conditions and used to produce simulated forecasts across the 30 year period. Thus, a reforecast provides evidence of what a forecast model would predict if it had been used to forecast environmental conditions at some previous point in time. As a result, the predictive skill of the forecast model can be evaluated at a variety of leads by comparing the predictions to the corresponding observed environmental conditions at that time.